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source("tianfengRwrappers.R")
library(future)
plan("multiprocess",workers = 8)
CA_dataset2 <- readRDS("CA_dataset2.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
human_coronary <- readRDS("human_coronary.rds")
Idents(human_coronary) <- human_coronary$samples
human_coronary <- RenameIdents(human_coronary,'1' = 'sample1','2' = 'sample2','3' = 'sample3','4' = 'sample4')
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$Classification1
ds2 <- readRDS("ds2.rds")

#sample info

ggsave("dataset2_sampleinfo.svg",plot = umapplot(CA_dataset2, split.by = "sample"), 
       device = svg, width = 25, height = 5)
ggsave("dataset1_sampleinfo.svg",plot = umapplot(CA_dataset1, split.by = "orig.ident"),
       device = svg, width = 15, height = 5)
ggsave("dataset0_sampleinfo.svg",plot = umapplot(human_coronary, split.by = "samples"),
       device = svg, width = 20, height = 5)

附图:所有marker基因表达热图 show 表达量最高的top5

logfc.threshold = 0.5, min.diff.pct = 0.3 pct.1>0.7

dataset2

svg(paste0("CA_dataset2_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset2_markers$gene, CA_dataset2, genes_to_show$gene,"CA_dataset2_supp")
dev.off()
null device 
          1 

dataset1

svg(paste0("CA_dataset1_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset1_markers$gene, CA_dataset1, genes_to_show$gene,"CA_dataset1_supp")
dev.off()
null device 
          1 

dataset0

human_coronary_markers <- FindAllMarkers(human_coronary, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)
Calculating cluster FB
Calculating cluster Macrophage
Calculating cluster EC
Calculating cluster SMC
Calculating cluster T cell
Calculating cluster B cell
Calculating cluster Neuron
Calculating cluster Plasma
human_coronary_markers <- human_coronary_markers[human_coronary_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- human_coronary_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("human_coronary_supp","_markers.svg"), height = 10, width = 15)
dhm2(human_coronary_markers$gene, human_coronary, genes_to_show$gene,"human_coronary_supp")
dev.off()
RStudioGD 
        2 

样本细胞比例

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[1:4]))+theme(
错误: 意外的')' in:
"  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[1:4]))"

XGBoost feature

pretrain

fea <- read.csv("./datatable/AC_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACpretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PA_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PApretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

model

fea <- read.csv("./datatable/ACtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PAtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PAmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
source("tianfengRwrappers.R")
library(future)
plan("multiprocess",workers = 8)
```

```{r}
CA_dataset2 <- readRDS("CA_dataset2.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
human_coronary <- readRDS("human_coronary.rds")
Idents(human_coronary) <- human_coronary$samples
human_coronary <- RenameIdents(human_coronary,'1' = 'sample1','2' = 'sample2','3' = 'sample3','4' = 'sample4')
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$Classification1
ds2 <- readRDS("ds2.rds")
```

#sample info
```{r}
ggsave("dataset2_sampleinfo.svg",plot = umapplot(CA_dataset2, split.by = "sample"), 
       device = svg, width = 25, height = 5)
ggsave("dataset1_sampleinfo.svg",plot = umapplot(CA_dataset1, split.by = "orig.ident"),
       device = svg, width = 15, height = 5)
ggsave("dataset0_sampleinfo.svg",plot = umapplot(human_coronary, split.by = "samples"),
       device = svg, width = 20, height = 5)

```


# 附图：所有marker基因表达热图 show 表达量最高的top5
### logfc.threshold = 0.5, min.diff.pct = 0.3 pct.1>0.7
dataset2
```{r}
CA_dataset2_markers <- FindAllMarkers(CA_dataset2, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)
CA_dataset2_markers <- CA_dataset2_markers[CA_dataset2_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- CA_dataset2_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("CA_dataset2_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset2_markers$gene, CA_dataset2, genes_to_show$gene,"CA_dataset2_supp")
dev.off()
```
dataset1
```{r}
CA_dataset1_markers <- FindAllMarkers(CA_dataset1, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)
CA_dataset1_markers <- CA_dataset1_markers[CA_dataset1_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- CA_dataset1_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("CA_dataset1_supp","_markers.svg"), height = 10, width = 15)
dhm2(CA_dataset1_markers$gene, CA_dataset1, genes_to_show$gene,"CA_dataset1_supp")
dev.off()
```


dataset0
```{r}
human_coronary_markers <- FindAllMarkers(human_coronary, logfc.threshold = 0.5, min.diff.pct = 0.3, only.pos = T)
human_coronary_markers <- human_coronary_markers[human_coronary_markers$pct.1>0.7,] %>% group_by(cluster) 

genes_to_show <- human_coronary_markers %>% group_by(cluster) %>% slice_max(n = 5, order_by = avg_logFC)

svg(paste0("human_coronary_supp","_markers.svg"), height = 10, width = 15)
dhm2(human_coronary_markers$gene, human_coronary, genes_to_show$gene,"human_coronary_supp")
dev.off()
```
# 样本细胞比例
```{r}
Idents(human_coronary) <- human_coronary$conditions
sp1 <- subset(human_coronary, idents = "sample1")
sp2 <- subset(human_coronary, idents = "sample2")
sp3 <- subset(human_coronary, idents = "sample3")
sp4 <- subset(human_coronary, idents = "sample4")
prop_mat <- cbind(prop.table(table(sp1$Classification1)),prop.table(table(sp2$Classification1)))
prop_mat2 <- cbind(prop.table(table(sp3$Classification1)),prop.table(table(sp4$Classification1)))
prop_mat <- cbind(prop_mat, prop_mat2)
colnames(prop_mat) <- levels(Idents(human_coronary))


plot_data = melt(prop_mat)
colnames(plot_data) = c('cell type','position','proportion')#修改每一列的名称

ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.5) + theme_bw()

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.3))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[3:6]) +theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 15, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 15, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

ggsave("human_coronary_prop.svg", device = svg, plot = prop_plot, width = 12, height = 6)

```


```{r}
Idents(CA_dataset1) <- CA_dataset1$orig.ident
Idents(CA_dataset1) <- c("sample1","sample2","sample3")

sp1 <- subset(CA_dataset1, idents = "sample1")
sp2 <- subset(CA_dataset1, idents = "sample2")
sp3 <- subset(CA_dataset1, idents = "sample3")
prop_mat <- cbind(prop.table(table(sp1$Classification1)),prop.table(table(sp2$Classification1)),prop.table(table(sp3$Classification1)))

colnames(prop_mat) <- levels(Idents(CA_dataset1))


plot_data = melt(prop_mat)
colnames(plot_data) = c('cell type','position','proportion')#修改每一列的名称

ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.5) + theme_bw()

prop_plot <- ggplot(plot_data, aes(x = `cell type`, y = proportion, fill = position)) + 
  geom_bar(stat = 'identity', position = "dodge", width=0.7) + coord_cartesian(ylim = c(0,0.6))+
  theme_bw() + scale_y_continuous(expand = c(0,0)) + scale_fill_manual(values = colors_list[3:6]) +theme(
    axis.title.x = element_text(size = 15), axis.text.x = element_text(size = 15, colour = "black"),
    axis.title.y = element_text(size = 15), axis.text.y = element_text(size = 15, colour = "black"),
    legend.text = element_text(size = 20), legend.title = element_blank(), panel.grid = element_blank())

ggsave("CA_dataset1_prop.svg", device = svg, plot = prop_plot, width = 12, height = 6)
```


## XGBoost feature
pretrain
```{r fig.width=6,fig.height=6}
fea <- read.csv("./datatable/AC_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACpretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PA_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PApretrain_features.png", device = png, plot = ggobj, width = 8, height = 8)
```

model
```{r fig.width=6,fig.height=6}
fea <- read.csv("./datatable/ACtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_AC,labels = "",label = F)
ggsave("ACmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)

fea <- read.csv("./datatable/PAtrain_features.csv")
ggobj <- multi_featureplot(fea$Feature[1:16],ds2_PA,labels = "",label = F)
ggsave("PAmodel_features.png", device = png, plot = ggobj, width = 8, height = 8)
```


    
Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
